Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared...
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Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integratio...
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(纸本)9798331314385
Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions. To this end, we train path-integrating recurrent neural networks (piRNNs) on a spatial navigation task, whose goal is to predict the agent's position with a special focus on rewarded locations. Grid-like neurons naturally emerge from the training of piRNNs, which allows us to investigate how the two aspects of the task, space and reward, are integrated in their firing patterns. We find that geometry, but not topology, of the grid cell population code becomes distorted. Surprisingly, these distortions are global in the firing patterns of the grid cells despite local changes in the reward. Our results indicate that after training with location-specific reward information, the preserved representational topology supports successful path integration, whereas the emergent heterogeneity in individual responses due to global distortions may encode dynamically changing environmental cues. By bridging the gap between computational models and the biological reality of spatial navigation under reward information, we offer new insights into how neural systems prioritize environmental landmarks in their spatial navigation code.
A few-mode VCSEL has an oxide-confined aperture of 6 μm and a heavily Zn-diffusion aperture of 4 μm, which is demonstrated a -3-dB E-O bandwidth (26.5 GHz), and passed 50 Gb/s pre-emphasis NRZ-OOK transmission over ...
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This research to practice paper describes two NSF-funded projects: The 'program for engineering Access, Retention, and LIATS Success' (PEARLS) and the 'Academic and Socioemotional Support Ecosystem for Tal...
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Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such appr...
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Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds...
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The process of speaker recognition in a noisy and distant environment is a difficult task as it faces numerous challenges before clean speaker speech signal reaching the microphone. While developing a deep neural netw...
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Adiabatic quantum-flux-parametron (AQFP) logic is a promising technology for future energy-efficient, high-performance information processing systems. It has significantly low power consumption thanks to the adiabatic...
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Integrating a large amount of Distributed Energy Resources (DER) on the distribution networks and even the utility-scale solar generation on the transmission networks of the modern electric grid poses significant chal...
Integrating a large amount of Distributed Energy Resources (DER) on the distribution networks and even the utility-scale solar generation on the transmission networks of the modern electric grid poses significant challenges and opportunities for utility grid operations. As system operations are siloed in transmission and distribution, the impacts of the DER and the effects of transmission events on distribution networks have necessitated the integrated study of the transmission and distribution networks. In addition, the limitations of conventional tools led to the approach of real-time system analysis for reliable operation. This paper aims to study the impacts of modeling inverter-based Resources (IBRs) and their responses in case of fault events. Finally, the results of a co-simulated integrated T&D approach with the IBRs have been provided to validate the successful simulation of T&D models to demonstrate different DER control and dispatch options to support new operations guidelines and standards.
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